To distinguish the lane identity of each pixel in the confidence branch, we predicted the embedding feature of each grid cell with reference to [1]}, [2]}. In the training of the network, based on distance metric learning, the distance between cell embeddings belonging to the same lane is small, whereas the distance between cell embeddings belonging to different lanes is maximized. In the inference of the network, we use a fast unsupervised clustering post-processing method to predict the variable number of lanes. Unlike front-view lanes that usually converge at vanishing point, 3D lane is more suitable for the embedding clustering loss function.
\(L_{embed}^{3d} = L_{var}^{3d} + L_{dist}^{3d}\) 
